



'''simple docstring'''





import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
 from PIL import Image


class 					snake_case			(lowerCamelCase__ ):
       lowerCAmelCase__	:str     =       "naver-clova-ix/donut-base-finetuned-docvqa"
       lowerCAmelCase__	:Optional[Any]     =       (
           "This is a tool that answers a question about an document (pdf). It takes an input named `document` which "
           "should be the document containing the information, as well as a `question` that is the question about the "
           "document. It returns a text that contains the answer to the question."
       )
       lowerCAmelCase__	:str     =       "document_qa"
       lowerCAmelCase__	:Any     =       AutoProcessor
       lowerCAmelCase__	:List[Any]     =       VisionEncoderDecoderModel

       lowerCAmelCase__	:List[Any]     =       ["image", "text"]
       lowerCAmelCase__	:List[Any]     =       ["text"]



       def __init__(					self  ,*UpperCAmelCase_  ,**UpperCAmelCase_   )	->				Dict:
           if not is_vision_available():
               raise ValueError("Pillow must be installed to use the DocumentQuestionAnsweringTool."   )

           super().__init__(*__snake_case  ,**__snake_case   )



       def 				_a   (					self  ,UpperCAmelCase_  ,UpperCAmelCase_   )	->				List[str]:
           lowercase__									=    '<s_docvqa><s_question>{user_input}</s_question><s_answer>'
           lowercase__									=    task_prompt.replace("{user_input}"  ,__snake_case   )
           lowercase__									=    self.pre_processor.tokenizer(
               __snake_case  ,add_special_tokens=__snake_case  ,return_tensors="pt"   ).input_ids
           lowercase__									=    self.pre_processor(__snake_case  ,return_tensors="pt"   ).pixel_values

           return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}



       def 				_a   (					self  ,UpperCAmelCase_   )	->				Tuple:
           return self.model.generate(
               inputs["pixel_values"].to(self.device   )  ,decoder_input_ids=inputs["decoder_input_ids"].to(self.device   )  ,max_length=self.model.decoder.config.max_position_embeddings  ,early_stopping=__snake_case  ,pad_token_id=self.pre_processor.tokenizer.pad_token_id  ,eos_token_id=self.pre_processor.tokenizer.eos_token_id  ,use_cache=__snake_case  ,num_beams=1  ,bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]  ,return_dict_in_generate=__snake_case  ,).sequences







       def 				_a   (					self  ,UpperCAmelCase_   )	->				Any:
           lowercase__									=    self.pre_processor.batch_decode(__snake_case   )[0]
           lowercase__									=    sequence.replace(self.pre_processor.tokenizer.eos_token  ,""   )
           lowercase__									=    sequence.replace(self.pre_processor.tokenizer.pad_token  ,""   )
           lowercase__									=    re.sub(R"<.*?>"  ,""  ,__snake_case  ,count=1   ).strip()  # remove first task start token
           lowercase__									=    self.pre_processor.tokenajson(__snake_case   )

           return sequence["answer"]









'''simple docstring'''






# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re

from ..models.auto import AutoProcessor
from ..models.vision_encoder_decoder import VisionEncoderDecoderModel
from ..utils import is_vision_available
from .base import PipelineTool


if is_vision_available():
     from PIL import Image

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """naver-clova-ix/donut-base-finetuned-docvqa"""
    lowercase__					=       (
        """This is a tool that answers a question about an document (pdf). It takes an input named `document` which """
        """should be the document containing the information, as well as a `question` that is the question about the """
        """document. It returns a text that contains the answer to the question."""
    )
    lowercase__					=       """document_qa"""
    lowercase__					=       AutoProcessor
    lowercase__					=       VisionEncoderDecoderModel

    lowercase__					=       ["""image""", """text"""]
    lowercase__					=       ["""text"""]







    def __init__( self					:    str		,     *__snake_case					:    Any		,     **__snake_case					:    List[Any]      ):
      if not is_vision_available():
        raise ValueError('Pillow must be installed to use the DocumentQuestionAnsweringTool.'      )

      super().__init__(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    Union[str, Any]		,     __snake_case					:    "Image"		,     __snake_case					:    str      ):
      a		:   Union[str, Any]	  =	'<s_docvqa><s_question>{user_input}</s_question><s_answer>'
      a		:   Any	  =	task_prompt.replace('{user_input}'		,     __snake_case      )
      a		:   Dict	  =	self.pre_processor.tokenizer(
          __snake_case		,     add_special_tokens=__snake_case		,     return_tensors='pt'      ).input_ids
      a		:   Tuple	  =	self.pre_processor(__snake_case		,     return_tensors='pt'      ).pixel_values

      return {"decoder_input_ids": decoder_input_ids, "pixel_values": pixel_values}







    def      lowercase_			( self					:    Any		,     __snake_case					:    int      ):
      return self.model.generate(
          inputs['pixel_values'].to(self.device      )		,     decoder_input_ids=inputs['decoder_input_ids'].to(self.device      )		,     max_length=self.model.decoder.config.max_position_embeddings		,     early_stopping=__snake_case		,     pad_token_id=self.pre_processor.tokenizer.pad_token_id		,     eos_token_id=self.pre_processor.tokenizer.eos_token_id		,     use_cache=__snake_case		,     num_beams=1		,     bad_words_ids=[[self.pre_processor.tokenizer.unk_token_id]]		,     return_dict_in_generate=__snake_case		,     ).sequences







    def      lowercase_			( self					:    List[str]		,     __snake_case					:    Tuple      ):
      a		:   str	  =	self.pre_processor.batch_decode(__snake_case      )[0]
      a		:   str	  =	sequence.replace(self.pre_processor.tokenizer.eos_token		,     ''      )
      a		:   Union[str, Any]	  =	sequence.replace(self.pre_processor.tokenizer.pad_token		,     ''      )
      a		:   List[str]	  =	re.sub(r'<.*?>'		,     ''		,     __snake_case		,     count=1      ).strip()  # remove first task start token
      a		:   Dict	  =	self.pre_processor.tokenajson(__snake_case      )

      return sequence["answer"]